Monte Carlo kilovoltage X-ray tube simulation: A statistical analysis and compact simulation method.
Phys Med
; 72: 80-87, 2020 Apr.
Article
en En
| MEDLINE
| ID: mdl-32229424
INTRODUCTION: Monte Carlo (MC) simulations are a powerful tool for improving image quality in X-ray based imaging modalities. An accurate X-ray source model is essential to MC modeling for CBCT but can be difficult to implement on a GPU while maintaining efficiency and memory limitations. A statistical analysis of the photon distribution from a MC X-ray tube simulation is conducted in hopes of building a compact source model. MATERIALS & METHODS: MC simulations of an X-ray tube were carried out using BEAMnrc. The resulting photons were sorted into four categories: primary, scatter, off-focal radiation (OFR), and both (scatter and OFR). A statistical analysis of the photon components (energy, position, direction) was completed. A novel method for a compact (memory efficient) representation of the PHSP data was implemented and tested using different statistical based linear transformations (PCA, ZCA, ICA), as well as a geometrical transformation. RESULTS: The statistical analysis showed all photon groupings had strong correlations between position and direction, with the largest correlation in the primary data. The novel method was successful in compactly representing the primary (error < 2%) and scatter (error < 6%) photon groupings by reducing the component correlations. DISCUSSION & CONCLUSION: Statistical linear transforms provide a method of reducing the memory required to accurately simulate an X-ray source in a GPU MC system. If all photon types are required, the proposed method reduces the memory requirements by 3.8 times. When only primary and scatter data is needed, the memory requirement is reduced from gigabytes to kilobytes.
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Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Método de Montecarlo
/
Tomografía Computarizada de Haz Cónico
Tipo de estudio:
Health_economic_evaluation
Idioma:
En
Revista:
Phys Med
Asunto de la revista:
BIOFISICA
/
BIOLOGIA
/
MEDICINA
Año:
2020
Tipo del documento:
Article
Pais de publicación:
Italia